Improving Monte Carlo Localization in sparse environments using structural environment information

Prestes, Edson; Ritt, Marcus; Führ, Gustavo

doi:10.1109/iros.2008.4651099

Cited by 8 publications

(6 citation statements)

References 14 publications

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“…However, we must point out that in sparse environments the thinning algorithm will not cover all free regions seen by the sensors. Thus, in this cases we recommend the additional use of partial thinning relaxations [16], to ensure that the robot will stay close to walls, delaying the entrance on featureless regions. Fig.…”

Section: Time Driven Bvp Integrated Explorationmentioning

confidence: 99%

Integrated exploration using time-based potential rails

Maffei

Jorge

Prestes

et al. 2014

2014 IEEE International Conference on Robotics and Automation (ICRA)

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Integrated exploration is the most complete task in mobile robotics, and corresponds to the union of mapping, localization and motion planning. A powerful integrated exploration solution must take into account decisions that improve the quality of the map construction, such as closing loops, at the same time that the environment is explored. Potential fields and boundary value problems (BVP) have been used with success in tasks of planning, localization and exploration, but not yet in integrated strategies. In this paper, we present an integrated exploration strategy using a time varying BVP-based exploration. Our strategy consists of creating potential rails that guide the robot to regions that are either unexplored or were visited a long time ago. We also apply local distortions in the potential field to generate a loop closure strategy. Experimental results demonstrate that our method improves the quality of the map construction, keeping the balance between revisiting and exploratory activities.

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Section: Time Driven Bvp Integrated Explorationmentioning

confidence: 99%

Integrated exploration using time-based potential rails

Maffei

Jorge

Prestes

et al. 2014

2014 IEEE International Conference on Robotics and Automation (ICRA)

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“…We observe that when L = 0, the robot navigates several steps inside region L 1 . This region is sparse and does not help the robot to localize itself properly [3]. While the other regions H 1 −H 4 are disregarded even though these regions have been visited many times (Figure 7(b)).…”

Section: A Experiments In a Sparse Environmentmentioning

confidence: 99%

“…The environment preferences can be used together with localization strategies to improve robot localization in problems of global localization [3]. In our case, we use the preferences to guide the robot in its mapping task favoring certain regions.…”

Section: Introductionmentioning

confidence: 99%

Exploration driven by local potential distortions

Prestes

Engel

2011

2011 IEEE/RSJ International Conference on Intelligent Robots and Systems

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Boundary Value Problem (BVP) Path Planners generate potential fields whose gradient descent represents navigational routes from any point of the environment to a goal position. The resulting trajectories are smooth and free of local minima. A BVP planner has been recently used to compute trajectories in known inhomogeneous environments, corresponding to different degrees of traveling difficulties. In this case, we locally distort the potential field generating regions with high or low preferences for navigating. In this paper, we extend this idea, generating an strategy allowing the robot to explore an unknown environment dynamically considering environment preferences. Several experiments demonstrate that our method can be used as a core in an integrated exploration approach.

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“…Grid localization approximates the posterior using a histogram filter over a grid decomposition of the pose space [21], [22], [23], [24], [7]. MCL is based on a particle filter that represents the posterior belief by a set of weighted samples (also called particles) distributed according to this posterior [25], [26], [27], [28], [29], [30], [31], [7], [32], [33], [34]. The crucial disadvantage of these two approaches is that they bear heavy online computational burden.…”

Section: Introductionmentioning

confidence: 99%

Self-adaptive Monte Carlo localization for mobile robots using range finders

Zhang¹,

Zapata²,

Lépinay³

2011

Robotica

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Abstract-In order to achieve the autonomy of mobile robots, effective localization is a necessary prerequisite. In this paper, we propose an improved Monte Carlo localization algorithm using self-adaptive samples, abbreviated as SAMCL. By employing a pre-caching technique to reduce the on-line computational burden, SAMCL is more efficient than regular MCL. Further, we define the concept of similar energy region (SER), which is a set of poses (grid cells) having similar energy with the robot in the robot space. By distributing global samples in SER instead of distributing randomly in the map, SAMCL obtains a better performance in localization. Position tracking, global localization and the kidnapped robot problem are the three sub-problems of the localization problem. Most localization approaches focus on solving one of these sub-problems. However, SAMCL solves all these three sub-problems together thanks to self-adaptive samples that can automatically separate themselves into a global sample set and a local sample set according to needs. The validity and the efficiency of the SAMCL algorithm are demonstrated by both simulations and experiments carried out with different intentions. Extensive experiment results and comparisons are also given in this paper.

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Improving Monte Carlo Localization in sparse environments using structural environment information

Cited by 8 publications

References 14 publications

Integrated exploration using time-based potential rails

Integrated exploration using time-based potential rails

Exploration driven by local potential distortions

Self-adaptive Monte Carlo localization for mobile robots using range finders

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